Identification of gene signatures and molecular mechanisms underlying the mutual exclusion between psoriasis and leprosy

Leprosy and psoriasis rarely coexist, the specific molecular mechanisms underlying their mutual exclusion have not been extensively investigated. This study aimed to reveal the underlying mechanism responsible for the mutual exclusion between psoriasis and leprosy. We obtained leprosy and psoriasis data from ArrayExpress and GEO database. Differential expression analysis was conducted separately on the leprosy and psoriasis using DEseq2. Differentially expressed genes (DEGs) with opposite expression patterns in psoriasis and leprosy were identified, which could potentially involve in their mutual exclusion. Enrichment analysis was performed on these candidate mutually exclusive genes, and a protein–protein interaction (PPI) network was constructed to identify hub genes. The expression of these hub genes was further validated in an external dataset to obtain the critical mutually exclusive genes. Additionally, immune cell infiltration in psoriasis and leprosy was analyzed using single-sample gene set enrichment analysis (ssGSEA), and the correlation between critical mutually exclusive genes and immune cells was also examined. Finally, the expression pattern of critical mutually exclusive genes was evaluated in a single-cell transcriptome dataset. We identified 1098 DEGs in the leprosy dataset and 3839 DEGs in the psoriasis dataset. 48 candidate mutually exclusive genes were identified by taking the intersection. Enrichment analysis revealed that these genes were involved in cholesterol metabolism pathways. Through PPI network analysis, we identified APOE, CYP27A1, FADS1, and SOAT1 as hub genes. APOE, CYP27A1, and SOAT1 were subsequently validated as critical mutually exclusive genes on both internal and external datasets. Analysis of immune cell infiltration indicated higher abundance of 16 immune cell types in psoriasis and leprosy compared to normal controls. The abundance of 6 immune cell types in psoriasis and leprosy positively correlated with the expression levels of APOE and CYP27A1. Single-cell data analysis demonstrated that critical mutually exclusive genes were predominantly expressed in Schwann cells and fibroblasts. This study identified APOE, CYP27A1, and SOAT1 as critical mutually exclusive genes. Cholesterol metabolism pathway illustrated the possible mechanism of the inverse association of psoriasis and leprosy. The findings of this study provide a basis for identifying mechanisms and therapeutic targets for psoriasis.


Data sources
Information on the annual incidence rate of leprosy and psoriasis were obtained from GBD 2019 database with the Global Health Data Exchange (GHDx) query tool (http:// ghdx.healt hdata.org/ gbd-resul ts-tool).
During the process of data integration, five datasets (GSE13355, GSE30999, GSE34248, GSE41662, GSE14905) detecting psoriasis gene expression using the GPL570 chip were integrated.Genes expressed in all samples were retained for further analysis.Batch effects were eliminated using the Combat function in the sva package 29 .
The national age-standardized incidence rates of leprosy and psoriasis were extracted from the GBD 2019 study, encompassing data from 204 countries.To visualize the data, world maps were created using the ggmap R package.

Identification of DEGs in psoriasis and leprosy
The R package DEseq2 was utilized to identify differentially expressed genes (DEGs) in psoriasis vs. normal and leprosy vs. normal, respectively.The criteria for selection were FDR < 0.05 and |Log2FC|> 1.

Candidate mutually exclusive genes signaling pathway enrichment analysis and PPI interaction network
The R software clusterProfiler 30 was employed to analyze the enrichment of gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways for the candidate mutually exclusive genes.The STRING database (https:// string-db.org/) was used to construct the PPI network, and interactions with a medium confidence score greater than 0.4 were considered statistically significant.Subsequently, Cytoscape was used to visualize the PPI network.The GeneMANIA database (https:// genem ania.org/) was utilized to identify critical mutually exclusive genes interaction patterns.

Internal validation
For the E-MTAB-10318 and GSE54456 datasets, receiver operating characteristic (ROC) curves for the hub genes were generated using the pROC package 31 .Additionally, the area under the curve (AUC) was calculated for each ROC curve.Box plots were created to compare the expression of hub genes between the case and the normal control group using appropriate software packages.Pathway and gene set enrichment analysis was also conducted using Hetionet v1.0 32 .

External validation
GSE74481 (66 leprosy and 9 normal control samples) from the GEO database was selected as the external validation set.The GSE13355, GSE30999, GSE34248, GSE41662, and GSE14905 datasets (214 psoriasis and 209 normal control samples) were integrated as part of the validation set.ROC curves associated with the hub genes were generated based on gene expression in the external validation set.Box plots were used to further illustrate the differences in gene expression between the case and the normal group.

Immune infiltration analysis
Using mRNA expression profiles normalized by DEseq2, single-sample gene set enrichment analysis (ssGSEA) in the R software GSVA package was employed to quantify the abundance of 28 immune cell types in psoriasis and leprosy samples.The cor.test function was used to calculate the correlation between hub genes expression levels and abundance of immune cells.

Single-cell data analysis
The expression matrix GSE150672 was obtained from the GEO database.Quality control and dimensionality reduction were performed using the Seurat software package (v4.1.1).During the initial quality control step, Seurat objects were created for the normal and psoriasis groups, and genes expressing < 200 were filtered out.Genes expressed in fewer than 3 cells were also excluded.The remaining cells' gene expression profiles were normalized, and 2000 hypervariable genes were identified from each sample using the VST method.All genes were scaled, and principal component analysis was performed.Unsupervised clustering (resolution = 0.5) was used to cluster the cells, and the top 20 principal components were visualized using Uniform Manifold Approximation and Projection (UMAP).Cell type annotation was performed using the singleR package (v1.8.1), with manual annotation for optimization.Single-cell level-based gene set enrichment analyses were integrated with Hub Genes using irGSEA (v1.1.2),with the enrichment score calculation method set to UCell.

Global epidemiology
Age-standardized incidence rates per 100,000 population for leprosy varied from 0 to 24.3 cases in 2019 people (Fig. 1A).Age-standardized incidence rates per 100,000 population for psoriasis varied from 12.9 to 251.7 cases in 2019(Fig.1B).There was an inverse association between the incidence rates of leprosy and psoriasis in the majority of countries.

Identification of DEGs
A total of 1098 DEGs were identified in leprosy transcriptome data, with 910 genes upregulated and 188 genes downregulated (Fig. 2A) (Table S2).Clustering analysis based on the expression levels of DEGs showed that most leprosy samples clustered together (Fig. 2B).In psoriasis transcriptome data, 3839 DEGs were identified, with 1641 genes upregulated and 2198 genes downregulated (Fig. 2C) (Table S3).Clustering analysis based on the expression levels of DEGs showed that psoriasis samples clustered together (Fig. 2D).
Vol:.( 1234567890 To identify mutually exclusive critical genes between psoriasis and leprosy, we took the intersection of genes with opposite expression direction in the two diseases.48 candidate mutually exclusive genes were identified by taking the intersection.Among them, 35 genes were upregulated in leprosy and downregulated in psoriasis (Fig. 2E), while 13 genes were downregulated in leprosy and upregulated in psoriasis (Fig. 2F).

Pathway enrichment analysis and PPI interaction network of candidate mutually exclusive genes
We performed KEGG and GO enrichment analysis on the 48 candidate mutually exclusive genes.Enriched pathways were selected based on adj.p. value < 0.05 and count > 1, resulting in 10 enriched KEGG pathways, including cholesterol metabolism, arachidonic acid metabolism, amino acid biosynthesis, insulin resistance, synaptic vesicle, etc. (Fig. 3A) (Table S4).Additionally, 24 GO terms were enriched, including biological processes (BP), cellular components (CC), and molecular functions (MF), such as monocarboxylic acid biosynthetic process, steroid esterification, specific granule, etc. (Fig. 3B) (Table S5).
Then, 48 candidate mutually exclusive genes were used to construct a PPI network using the STRING database.The genes within this network mainly involved in vitamin K metabolic process, Lipoprotein metabolism (Table S6).APOE, CYP27A1, FADS1, and SOAT1 were identified as hub genes after applying the degree filter.(Fig. 3C).Further analysis of these genes using the GeneMANIA database revealed their involvement in processes such as regulation of plasma lipoprotein particle levels, sterol homeostasis, clearance of plasma lipoprotein particles, etc. (Fig. 3D).

External validation
In order to verify the robustness and credibility of our result, leprosy GSE74481 dataset, psoriasis integrated dataset and GSE66511 were used for external validation.Based on the expression trends and AUC values across all datasets, APOE, CYP27A1, and SOAT1 were determined to be critical mutually exclusive genes.The ROC curves showed that APOE, CYP27A1, and SOAT1 had high AUC percentages in the GSE74481 dataset (reaching 99.2%, 91.5%, and 85.1%, respectively) and low AUC percentages in the psoriasis integrated dataset (reaching 15.0%, 15.0%, and 29.2%, respectively) (Fig. 4C and G).Box plots showed APOE, CYP27A1, and SOAT1 was upregulated in leprosy tissues, whereas their expression was downregulated in psoriasis tissues (Fig. 4D and H).In GSE66511 dataset, APOE, CYP27A1, FADS1, and SOAT1 were also significantly downregulated in psoriasis (Fig. S1A).GSE66511 displayed the significant diagnostic value of those genes in psoriasis (Fig. S1B).

Analysis of immune cell infiltration
Subsequently, we quantified immune cell infiltration in leprosy and psoriasis using ssGSEA.The results showed that the level of immune cell infiltration in leprosy was generally higher compared to normal control samples (Fig. 6A).Among the 28 types of immune cells analyzed, 19 showed higher infiltration levels in leprosy compared to normal controls (Fig. 6B).Similarly, the level of immune cell infiltration in psoriasis was generally higher than in normal control samples (Fig. 6C), with 23 out of 28 immune cell types exhibiting higher infiltration levels in psoriasis compared to normal controls sample (Fig. 6D).Notably, 16 immune cell types showed higher infiltration levels in both leprosy and psoriasis samples compared to normal controls.Furthermore, we investigated the correlation between critical mutually exclusive genes expression levels and immune cell infiltration levels in psoriasis and leprosy.In leprosy, the expression levels of APOE and CYP27A1 were positively correlated with the levels of 18 immune cell types.Memory B cells and central memory CD8 T cells showed a negative correlation with the expression levels of APOE and CYP27A1.SOAT1 exhibited a positive correlation with 8 immune cell types (Fig. 6E).In psoriasis, APOE expression levels showed positive correlations with the levels of 10 immune cell types and negative correlations with 6 immune cell types.CYP27A1 expression

Analysis of critical mutually exclusive genes expression at single cell level
We analyzed the expression of critical mutually exclusive genes in different cell clusters in the single-cell transcriptome dataset.After cell annotation, a total of 12 cell types were identified across the samples (Fig. 7A).The marker gene of 12 cell types was list in Table S9.The top 10 marker gene of Fibro and KC were highly expressed in isolated fibroblasts and keratinocytes cell, respectively, in dataset GSE94655 (Fig. S2).The critical mutually exclusive genes were mainly expressed in Schwann cells (Fig. 7B).Schwann cells and fibroblasts had the highest cell density across all samples (Fig. 7C).The proportion of Schwann cells was higher in leprosy compared to psoriasis samples (Fig. 7D).

Discussion
Psoriasis and leprosy rarely coexist, and there is evidence of an inverse association between these two conditions in terms of epidemiology, immunology, and genetics.However, the underlying mechanism is poorly understood.
Here, the first time to our knowledge, we conducted an integrated analysis of the transcriptomes of psoriasis and leprosy to identify mutually exclusive critical pathways, genes, and cells between those two conditions.Our findings revealed that the mutually exclusive genes were predominantly enriched in pathways related to cholesterol www.nature.com/scientificreports/metabolism, fatty acid metabolism, and other processes.Among DEGs, APOE, CYP27A1, and SOAT1 confirmed as critical mutually exclusive genes, exhibiting opposite expression patterns in psoriasis and leprosy.Notably, these genes were mainly expressed in Schwann cells.Our study sheds light on potential mechanisms underlying the mutual exclusion between psoriasis and leprosy, providing a foundation for future research in this field.By analyzing the transcriptomes of psoriasis and leprosy, we identified 45 genes with opposite expression trends in the two diseases.Functional enrichment analysis revealed that they are involved in the cholesterol metabolism pathway.Previous studies have indicated higher cholesterol concentration in psoriatic lesions compared to normal controls 33 , and the accumulation of cholesterol in keratinocytes inhibits cholesterol and fatty acid biosynthesis 34 .Additionally, M. leprae can alter host lipid metabolism, facilitating infection and bacterial persistence 35,36 .Cholesterol concentration in leprosy lesions is also elevated compared to normal controls 37 .Previous studies have shown upregulation of genes associated with cholesterol and fatty acids in leprosy 38 .Thus, cholesterol metabolism may exhibit a mutually exclusive pattern in psoriasis and leprosy.
Four hub genes APOE, CYP27A1, FADS1 and SOAT1 were downregulated in psoriasis, while were upregulated in leprosy.When validating hub genes in an external dataset, three critical mutually exclusive genes were identified, namely APOE, CYP27A1, and SOAT1.Six isoforms from 4 hub genes exhibit opposite expression trends in psoriasis and leprosy groups.The isoform accounting for the majority of the hub gene expression belongs to these six isoforms.We observed that identical genes and their specific RNA variants are expressed differentially in both conditions, aligning with the findings of the Kõks et al. 39 .
All three critical mutually exclusive genes were involved in cholesterol metabolism pathway.Among them, APOE encodes apolipoprotein E, which plays a crucial role in lipoprotein transport and lipid metabolism 40 .APOE downregulation has been observed in psoriatic lesions, contributing to the proliferation of epidermal keratinocytes and the formation of psoriatic plaques 41,42 .On the other hand, APOE has been associated with leprosy 43 .Single-cell transcriptome sequencing has showed APOE upregulated in leprosy lesions 44 .Plasma lipoproteins elevation promote the survival of M. leprae 36 .
CYP27A1 encodes cytochrome P450 oxidase, which is involved in the synthesis of cholesterol, steroids, and other lipids 45 .Yu et al. 46 found CYP27A1 was downregulated in psoriasis.CYP27A1 hydroxylates cholesterol 47 and elevated cholesterol levels in psoriatic lesional skin play a critical role in IL-17A signaling and suppress cholesterol and fatty acid biosynthesis genes 34 .While, CYP27A1 in M. leprae host cells may oxidize cholesterone produced by M. leprae, regulating host cell functions and promoting the invasion and persistence of M. leprae 48 .www.nature.com/scientificreports/SOAT1 encodes Sterol O-Acyltransferase 1, which is essential for maintaining cellular cholesterol homeostasis 49 .SOAT1 was downregulation in psoriasis skin lesions 50 , The function SOAT1 in psoriasis and leprosy has not been extensively studied.SOAT1 expression level was positively correlated with macrophages, neutrophils, Th17 cells, and activated dendritic cells, while negatively correlated with plasmacytoid dendritic cells or natural killer cells in glioma 51 .Inhibition of SOAT1 expression can have anti-inflammatory effects by altering free cholesterol levels or oxysterol levels 52 .
We further analyzed the differential immune cells between leprosy and normal control samples, as well as psoriasis and normal control samples.The infiltration levels of 16 immune cell types were found to be higher in both leprosy and psoriasis samples compared to normal controls.Transcriptome analysis of psoriasis demonstrated a significant increase in γδT cells, resting NK cells, M0 macrophages, M1 macrophages, activated dendritic cells, and neutrophils in the skin 53,54 .While, immune cell infiltration in leprosy has not been extensively reported.www.nature.com/scientificreports/leprosy patients.Recent studies have shown that peptides derived from human APOE exhibit immunomodulatory and antibacterial effects 55 .CYP27A1 is highly expressed in bone marrow immune cells and macrophages and promotes breast cancer by impairing T cell expansion 56 .
At the single-cell level, the expression levels of APOE, CYP27A1, and SOAT1 were found to be highest in Schwann cells and fibroblasts.Schwann cells not only support repair and promote axonal regeneration in the peripheral nervous system but also contribute to the dissemination of Bacillus leprae in leprosy patients 57 .The increased cholesterol in leprosy patients leads to lipid accumulation in Schwann cells in the form of lipid droplets, regulating inflammation and immune responses 35 .Leprosy involves peripheral nerves at some point during the course of the disease 58 , while patients with psoriasis may experience remission following nerve injury 28 .
Furthermore, mutually exclusive genes were found to be enriched in lipoprotein metabolism and the vitamin K metabolism pathway.Notably, vitamin K has been recognized for its role in sphingolipid formation 59 .Sphingolipid regulate a diverse range of processes such as cell proliferation, differentiation, inflammation, endocytosis, and neural transmission.For instance, in psoriasis, elevated levels of S1P can inhibit the proliferation of keratinocytes involved in the formation of the stratum corneum and promote their differentiation, S1PR1 modulators can ameliorate psoriasis 60,61 .Mycobacterium leprae, the causative agent of leprosy, is capable of synthesizing sphingolipids to enhance its entry into host cells 62 .The bacterium interacts with specific sphingolipids on the cell membrane, potentially facilitating its invasion of macrophages and Schwann cells 63,64 and, consequently, leading to infection.In the context of leprosy, the bacterium can infect Schwann cells, resulting in damage to peripheral nerves 65 .Schwann cells are acknowledged for their sphingolipid production 66 , and their infection may disrupt the delicate balance of sphingolipids in nerve cells, potentially contributing to nerve damage.In addition, Mycobacterium leprae has been demonstrated to exert influence on host immune responses by altering the host's sphingolipid metabolism 67 , thereby impacting the immune system's efficacy in combating the infection.
In summary, our study utilized transcriptome data to elucidate the mutually exclusive mechanisms underlying leprosy and psoriasis.Notably, APOE, CYP27A1, and SOAT1 identified as candidate mutually exclusive genes between the two diseases, with their involvement in cholesterol metabolism pathways.These findings provide a theoretical basis for further research in this field and contribute to our understanding of the mutual exclusion between leprosy and psoriasis at a molecular level.However, it is important to note that our study has limitations, such as the limited information available in public databases and the reliance on bioinformatics analysis.Further experimental verification is necessary to confirm our findings, despite the support from previous studies.

Conclusion
Current bioinformatics studies have provided valuable insights into the transcriptomic profiles of leprosy and psoriasis, revealing potential mutually exclusive genes between these two diseases.Among these genes, APOE, CYP27A1, and SOAT1 have been identified as critical mutually exclusive genes and are associated with cholesterol metabolism pathways.These findings suggest the molecular mechanism that contributes to the mutual exclusion of leprosy and psoriasis, providing a foundation for further research in this field. https://doi.org/10.1038/s41598-024-52783-0www.nature.com/scientificreports/ https://doi.org/10.1038/s41598-024-52783-0www.nature.com/scientificreports/

Figure 1 .
Figure 1.Incidence estimates of leprosy and psoriasis per 100,000 population by country in 2019.(A) Incidence of leprosy in 2019; (B) Incidence of psoriasis in 2019.

Figure 2 .
Figure 2. Results of differential expression analysis.(A) Volcano plot of differential expression in leprosy; (B) Heatmap of differentially expressed genes in leprosy; (C) Volcano plot of differential expression in psoriasis; (D) Heatmap of differentially expressed genes in psoriasis; (E) Venn diagram of up-regulated genes in leprosy and down-regulated genes in psoriasis; (F) Venn diagram of down-regulated genes in leprosy and up-regulated genes in psoriasis.

Figure 3 .
Figure 3. Pathway enrichment and PPI network.(A) Chord diagram of KEGG enrichment analysis; (B) Bubble diagram of GO enrichment analysis; (C) PPI network diagram; (D) hub genes interaction diagram.

Figure 4 .
Figure 4. ROC curve and expression level of hub genes.(A) ROC curve of 4 genes in E-MTAB-10318; (B) Expression level of 4 genes in E-MTAB-10318; (C) ROC curve of 4 genes in GSE74481; (D) Expression level of 4 genes in GSE74481.(E) ROC curve of 4 genes in GSE54456; (F) Expression level of 4 genes in GSE54456; (G) ROC curve of 4 genes in psoriasis integrated dataset; (H) Expression levels of 4 genes in the psoriasis integrated dataset.

Figure 5 .
Figure 5. Expression level of hub genes transcripts.(A) Expression level of 4 hub genes transcripts in E-MTAB-10318, (B) Expression level of 4 hub genes transcripts in GSE54456.

Figure 6 .
Figure 6.Analysis of immune cell infiltration in leprosy and psoriasis.(A) Heatmap of immune infiltration in leprosy; (B) Boxplot of differences in immune infiltration between leprosy and normal controls group; (C) Heatmap of immune infiltration in psoriasis; (D) Boxplot of differences in immune infiltration between psoriasis and normal controls group; (E) Correlation between critical mutually exclusive genes expression levels and immune cell abundance in leprosy; (F) Correlation between critical mutually exclusive genes expression levels and immune cell abundance in psoriasis.

Figure 7 .
Figure 7. Critical mutually exclusive genes expression at single-cell level.(A) UMAP representation of cell distribution from psoriasis, leprosy and normal skin; Clusters are distinguished by different colors, with the identity of each cell cluster shown on the right.(B) Critical mutually exclusive genes expression in different types of cells.(C) Density scatterplot of critical mutually exclusive genes set; (D) The proportion of each type of cell is different among each group.